AgroStar has launched a cloud-based mobile app that is helping to boost crop yields and encourage best practices for small farmers in India. Launched as an on-premises ecommerce platform selling farm tools in 2008, the firm turned to Google Cloud Platform (GCP) to expand its offering. It now uses cloud-based analytics and is deploying ML models to provide timely advice in five languages on everything from seed optimization, crop rotation, and soil nutrition to pest control.

A 2018 survey underscored the demand for agricultural planning for Indian farmers. While farming remains a dominant sector in India, employing half of its labor force, 70 percent of small farmers – those cultivating fewer than three acres – said their crops are damaged by unforeseen weather and pests. An even higher number – 74 percent – say they lack access to farming-related information.

Widening that gap is the relative lack of access to new, higher yield seeds and improved soil analyses for small farmers, who must otherwise rely on traditional methods. “It could take a few years for innovative information to trickle down from universities to small, grassroots farmers,” says Pritesh Gudge, AgroStar Software Engineer. “Today, just by clicking through our Android application, farmers learn about new, effective farming practices and receive advice customized to their crop and soil.”

Connecting a million farmers in the cloud

Operating in the Indian states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka, AgroStar is closing the knowledge gap with a full-service, cloud-based SaaS solution – the only one of its kind in India. It combines agronomy, data science, and analytics to help farmers by providing a variety of resources.

AgroStar has reached over a million farmers through its Android app, the AgroStar Agri-Doctor. The mobile client is available as a web-based or full-featured native app. Both provide access to the firm’s knowledge base hosted on GCP, a Q&A forum that connects farmers to each other to help understand and better solve problems and to learn about innovative practices and products. Farmers can also click through to follow local and national market trends that help forecast crop prices.

In addition to the self-service knowledge base, AgroStar provides access to agronomy experts who use cloud-based analytics tools and historical data to provide season-and locale-specific advice to each farmer. “We are now tracking thousands of calls in 5 languages each day,” says Pritesh.

The AgroStar app also provides links to purchase and then track the delivery of farm tools and supplies such as cultivators and fertilizers. An in-house platform manages fulfillment centers and a doorstep delivery network simplifies the supply chain while giving farmers what they need, when they need it. By procuring directly from the manufacturers and primary distributors of farm supplies, Agrostar is achieving cost savings, which it passes on to farmers.

Build fast, pivot faster

From the start, the human and environmental variables of farming in India, not to mention the volume of AgroStar’s few hundred thousand monthly active users, made a highly scalable cloud-based solution inevitable. Farmers rely on the firm’s Agri-Doctor app to provide advice in multiple languages on topics that range widely throughout three growing seasons, each with distinct crop nutrition and rotation cycles and farm implementation requirements.

“For farmers, the focus keeps changing every month, and every season,” says Pritesh. “To serve our growing community, we needed a platform that could process images at high volume, fulfill tools and seed orders across thousands of miles, and respond to multilingual queries. We quickly moved away from spreadsheets and server-based solutions – we needed to build fast and pivot faster.”

Ending late-night deployments

The firm’s first cloud experience was with an AWS solution. At the time, AWS was the only cloud provider in India, but AgroStar wanted to find a solution that was easier to use and offered better integration with Android devices. “Deployment and processing costs were very high, and the developer tools and documentation were not as intuitive as we needed,” says Pritesh.

When GCP service arrived in India in October 2017, AgroStar embarked on a platform re-implementation that made possible dramatic changes in the way it developed and deployed its solution. Using Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services, the firm built the backend for the Agri-Doctor discussion forum in only three weeks. The platform’s microservice architecture is implemented in Python and Golang and deployed on GCP.

AgroStar began to realize significant efficiencies in its build, deploy, and test cycles. “We previously needed to work overnight to deploy to production,” says Pritesh. “Now using Google for Kubernetes containers and a rolling update strategy, we can deploy during the day without any problems or interruptions to service.”

The move to GCP streamlined AgroStar’s stack. “We were running 12 independent instances on AWS,” says Pritesh. “With Google Kubernetes Engine, we are deployed on a single cluster at a cost savings of $1,300 per month and growing.”

Improving customer response times by 85 percent

With a managed deployment capability, AgroStar can devote more time and resources to executing on its platform and Agri-Doctor app development plan. A strategic goal was managing customer response times as the firm grew its base. GCP has helped the firm meet that goal, achieving an 85 percent improvement in customer response times even as traffic grew significantly.

“With our on-premises solution, we could handle around 100 customers daily, which took 30 to 50 minutes for each customer,” says Pritesh. “We now handle thousands of customers daily, taking only 4 to 5 minutes for each one.”

AgroStar used Firebase to implement its Agri-Doctor app. A real-time cloud database, Firebase provides an API that enables the Agri-Doctor advice forum to be synchronized across all its far-flung mobile clients, effectively sharing knowledge base updates with one million users in near real time.

Using cloud tools to manage and monitor

Cloud Pub/Sub, Kafka, and Cloud Dataflow manage data ingestion and queueing of event and transaction data to the analytics layer. BigQuery fetches and persists data to Cloud Storage. Cloud SQL and dashboards powered by Tableau deliver farmer crop and soil profiles within minutes.

Cloud IAM helps AgroStar control access to all its cloud resources. And Stackdriver, the integrated logging aggregation capability for GCP, helps monitor and speed debugging on every tier of the AgroStar solution.

Machine learning to enhance yields

AgroStar is developing a variety of ML components to improve responsiveness and extend its platform offerings.

To speed up the diagnosis of and treatment for crop blight, AgroStar is building a deep learning pipeline using TensorFlow. The pipeline relies on GoogLeNet models that use multi-layered convolutional visual pattern recognition. It will assess uploaded images to support a disease-detection capability on the mobile app. Based on the commercially successful AI algorithms that automated postal code processing, GoogLeNet offers improved performance and computational efficiencies by using a creative layering technique that distinguishes them from older, sequential recognition engines.

To improve its customer search experience, AgroStar is developing an ML pipeline that shrinks fetch times by suggesting tags mapped to stored data. Processed using TPUs, Cloud Natural Language and Video AI, the tags provide a metadata layer that supports queries in any of the ten natural languages that AgroStar farmers can use.

The AgroStar search pipeline consists of Long Short-Term Memory (LSTM) models of Recurrent Neural Networks. Recurrent networks exhibit “memory” through iterative processing and are distinguished from feedforward networks by a feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input.

Implementing a recommendation engine

The firm is also adapting the Random Forests TensorFlow AI model to develop a crop and product recommendation engine. The model is trained by consuming numerical (rainfall, humidity, water availability per acre) and categorical (soil type, water sources) parameters to suggest appropriate products by season, region, and locale.

To simplify the product suggestion experience, AgroStar developers are testing Cloud Dialogflow, the Google Cloud conversational interface, to build a chatbot capability into its mobile app. The bot will track a farmer’s crop schedules and answer simple questions by linking to the recommendation engine.

AgroStar is also extending its analytics platform with AI-powered sales planning and forecasting. Using linear regression models implemented in TensorFlow and powered by Cloud ML Engine, the capability will enhance supply chain logistics as the company scales its operations across India.

To provide a credit on-demand offering for a range of seed-to-harvest cycle products, AgroStar is attempting to use Vision API to create an AI model that will convert uploaded photos of customer application records into standard data formats. The firm’s credit policy features a grace period in which farmers begin paying back loans after harvested crops go to market.

A versatile and friendly development ecosystem

AgroStar credits the convivial tools and documentation that GCP offers and its incremental, pay-as-you-go pricing model for both the firm’s success and its ability to manage growth.

“What Google Cloud offers is extremely good documentation and extremely simple-to-use tools and interfaces across all services,” says Pritesh. “It helped us initially deploy our platform and at every scale that we have required since then, and its cost effectiveness enabled us to staff up to meet new feature milestones.”

2021 has been a high-stakes year for call centers, with many organizations forced to rapidly scale up their call center operations in response to ongoing pandemic disruptions. We’re proud that 2021 has also been an amazing year for Google Cloud’s Contact Center AI (CCAI), which has helped our customers adapt and thrive, despite the challenging conditions. 

Beginning in January, we launched Dialogflow CX in GA. Agent Assist preview was released in May. Most recently, CCAI Insights GA was announced at Google Cloud NEXT in October. During NEXT, we shared lots of great content on how you can use CCAI to improve your customer experience with these breakout sessions:

• Using CCAI Insights to Better Understand Your Customers 
• Customer Impact with Conversational AI 
• Drive Results by Transforming the Customer Experience with AI-Powered Business Messages

But don’t just take our word for it. We also got a chance to hear how some companies are using CCAI to better reach their own customers, including The Home Depot, TELUS, and Love Holidays. We partnered with CDW to discuss  transforming the contact center with AI and with Quantiphi on how to migrate from Dialogflow EX to CX. Our integration with Looker Block also makes CCAI Insights even more powerful by visualizing contact center metrics. Over the summer, we hosted a Dialogflow CX competition with more than 1,100 participants. Just last month, we showed how we’ve enabled businesses to use AI in their interactions using Google Business Messages. Looking to the future, we talked about the future in our article,“Reimagining your Customer Experience with Conversational AI.”  

Amwell,  a U.S.-based telehealth company that is launching CCAI, including the recently launched CCAI Insights, is among the enterprises harnessing AI to transform its call centers. “With Contact Center AI, we aim to digitize our support for improved operational efficiency and elevated analytics capabilities, while enhancing the customer experience for patients, providers, and staff,” says Paul Johnson, SVP Client Services at Amwell. “Contact Center AI Insights will allow Amwell to better understand why our platform users are reaching out to support and how they feel about the overall experience – valuable insights for our support organization.”

As we recap the momentum of CCAI for 2021, it’s also a good time to review exactly how CCAI works.

What is CCAI?

As the volume of customer calls increases, it’s becoming even more important to make the most of human agents’ time to lower costs and improve customer experiences. CCAI enables you to do just that: it frees human agents to concentrate on more complex calls by providing them with real-time information to better handle those calls. 

Single source of intelligence: Contact Center AI provides a consistent, high-quality conversational experience across all channels and platforms, both human and virtual. Because the “brains” of CCAI are centralized in the cloud, you can apply consistent intelligence across every application in the customer journey. 

Ability to go off-script: Huge cost savings can be realized by having a virtual agent handle voice calls. This is easier said than done, however,  because conversations rarely are completely linear; instead, they meander from topic to topic, which is difficult to handle programmatically in a fixed-path Interactive Voice Response (IVR) system.   

Contact Center AI has the ability to go “off script” — to let callers go down tangents or side paths to the main conversation, while still tracking towards the main objective of the call.  With CCAI, your virtual agents can answer complex questions and complete complicated tasks, including allowing for unexpected stops and starts, unusual word choices, or implied meanings.  Developers can define supplemental questions, and CCAI can easily retain the context, answer the supplemental question, and come back to the main flow.

Versatile fulfillment: CCAI has the ability to handle multiple use cases for the customer with the same virtual agent, which enables you to fully automate routine tasks and deflect calls.  The same virtual agent can take a payment, update information like a phone number, give a customer information on their balance, and process information for other tasks, all within the same conversational flow.

How does CCAI work?

CCAI has three key components:

• Conversation Core: This is the central AI brain that underpins CCAI and its ability to understand, talk, and interact.  It enables and orchestrates high-quality conversational experiences at scale making it possible for customers to have  conversations with a virtual agent that are as good as conversations with a human agent.
  • Understand – Speech-to-text speech recognition understands what customers are saying regardless of how they phrase things, what vocabulary they use, what accent they have, and so on. 
  • Talk – Text-to-speech enables virtual agents to respond to customers in a natural, human-like manner that pushes the conversation along, rather than frustrate them.
  • Interact – Dialogflow identifies customer intent and determines the appropriate next step. You can build conversational flows in a point-and-click interface, and generate automated ML models for human-like conversational experiences.
• Virtual agents with Dialogflow: This component automates interactions with customers, using natural conversation to identify and address their issues. Virtual agents enable customers to get immediate help anytime, day or night. Agent Assist: This component brings AI to human agents to increase the quality of their work, while decreasing their average handling time.  Agent Assist shares initial context and provides real-time, turn-by-turn guidance to coach agents through business processes, as well as full call transcriptions that agents can edit and file quickly. 
• CCAI Insights: CCAI Insights aids your contact center management team in making better data driven decisions for their business by breaking down conversations using natural language processing and machine learning. Having this information allows your business to reduce manual analysis and focus on decision making like which conversations need your attention, where to deploy virtual agent automation to have the biggest impact and how to address your customer needs.  

How does CCAI create experiences for agents and customers? 

When a user initiates a chat or voice call and the contact center provider connects them with CCAI, a virtual agent engages with the user, understands their intent, and fulfills the request by connecting to the backend. If necessary, the call can be handed off to a human agent, who sees the transcript of the interaction with the virtual agent, gets feedback from the knowledge base to respond to queries in real time, and receives a summary of the call at the end. Insights help you understand what happened during the virtual agent and live agent sessions. The result is improved customer experiences and CSAT scores, lower agent handling times, and more time for human agents to spend on more complicated customer issues.

And there you have it: a quick overview of CCAI and its progress in 2021. For more details, check out the documentation or our CCAI solutions page.https://www.youtube.com/embed/6_Gilug2QYw?enablejsapi=1&

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

While there is uncertainty about how much the climate will change in the future, we know it won’t look like the past. Extreme weather events will increase in frequency and severity; the world will continue to warm, and the cost of climate change will increase.

Government plays a vital role in understanding and responding to these changes quickly. Achieving this improved response time will require data insights to ensure informed decision-making—from local to global scales. The challenge is not only urgent; it’s one of the world’s biggest “big data” problems.

Fortunately, new technologies to help us monitor the Earth are proliferating. Thousands of satellites take millions of images of the planet every day. Sensors generate data about temperature, precipitation, wind, soil conditions, and more—as frequently as every second. We have more information about the planet’s systems than at any other time in history. And the data will only continue to grow. The problem is not the lack of data–it is harnessing this data to drive insights for decision makers to tackle climate change. That’s why Google Cloud has partnered with Climate Engine.

How Climate Engine and Google Cloud enable greater climate resilience

Climate Engine is a scientist-led company that works with Google to accelerate and scale the use of Google Earth Engine’s world-class geospatial capacities (in addition to those of Google Cloud Storage and BigQuery, among other tools) in support of climate action in the public sector. Powered by Google Cloud’s infrastructure, Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities, enabling scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth’s surface. 

With cloud-based technologies, we can leverage massive computing at a scale that generates actionable insights from Earth-based data. These insights help us better manage resources, understand risks, predict changes, and respond to disasters as we meet the challenge of climate change. Geospatial AI combines the power of artificial intelligence (AI) and machine learning (ML) with geospatial analysis. Google Cloud’s Geospatial AI solutions provide departments and agencies with a centralized system to collect, process, and deliver Earth-based data into decision-making contexts.

Climate Engine and Google Cloud provide specialists with the opportunity to go back in time and see how our landscapes have changed due to changes in climate and other human activities over the past few decades. Years of data can now be quantitatively analyzed and visualized in a matter of a few seconds, enabling government agencies to fulfill their mandates by drawing invaluable insights into how landscapes are changing, what physical and natural assets are at risk, and where the opportunities are for reducing emissions and increasing carbon sequestration. 

“This is game changing for natural resource managers and scientists at public institutions at all levels of government,” says Dr. Daniel McEvoy, regional climatologist, at the Desert Research Institute & Western Regional Climate Center, Nevada System of Higher Education.https://www.youtube.com/embed/aPGsi8bd_Zk?enablejsapi=1&

Use cases for geospatial climate information systems

The use cases for this technology are as varied as the climate challenges themselves. These include monitoring, predicting, and analyzing the risks of extreme weather events like floods, wildfire, drought, extreme heat, wind, and other climate hazards. Use cases also include tracking changes in ecosystems, disease vectors, water availability and quality, soil health, growing seasons, air pollution, and more. These use cases are some of the ways that Google Cloud and Climate Engine can help the public sector deliver on government mandates. These provide insights that are helpful for a wide range of departments, and that can be applied in spatial and temporal scales that are meaningful for governments to take action.

“Our planet is changing at a rate that we have never experienced,” says Forrest Melton of the NASA Western Water Applications Office. To respond to these changes, we must understand what is happening across a wide range of environmental variables and at geospatial scales that range from local to global. We now have access to more data about the planet than ever before. The big challenge is converting data into actionable insights and then rapidly integrating these insights into decision-making systems. Climate Engine and Google Cloud help resolve this problem through innovative analytical tools and effective use of cloud computing.” 

Climate change carries an existential risk to our current and future stability and security. Together, we are working to provide transformational technologies that help meet that risk and build a safer, more resilient future for all of us. 

Learn more about Google Cloud’s environmental initiatives here and here.

In recent years, machine learning (ML) has gained tremendous popularity as a powerful tool for solving complex problems across various domains. However, building and deploying ML models at scale can be challenging, as it involves a range of tasks such as data preparation, feature engineering, model training, deployment, monitoring, maintenance and so on. 

According to “The Art of AI maturity” report published by Accenture “87% of data science projects never make it into production.” This is where MLOps comes in – it can help to address the core challenges by providing a framework for managing the entire ML lifecycle, from data collection and preparation to model development, testing, and deployment. It also reduces the time from ML model development to production and increases the success rate of ML projects.

In Google Cloud, we understand how important MLOps is to successfully productionize ML models. So we collaborated with Equinix, the world’s digital infrastructure company™ and  a leader in global colocation data center market share, with 248 data centers in 27 countries on five continents. We helped them by providing the advisory services on the MLOps best practices and architecture. 

Let’s take a sneak peek at the MLOps requirements at Equinix and the final architecture that was proposed.

What does MLOps mean for Equinix?

After multiple discovery sessions with the Equinix Team, we identified the core requirements and pain points to address in their new MLOps architecture:

• Reusability: Components such as features and pipeline components should be reused across projects to reduce costs and improve efficiency.
• Foundations: The foundations of the infrastructure, such as environments, folder structure, and project hierarchy, should be well-designed to support scalability and reliability.
• Early identification of problems: Problems should be identified early by including data validation, notifications, and retry mechanisms.
• Cost optimization: Costs should be optimized by paying only for what is used.
• Enterprise CI/CD requirements: Enterprise CI/CD requirements should be met by integrating with GitHub and GitActions.
• Scaling: The infrastructure must scale to support future growth.
• Security: Enterprise security requirements should be met in terms of IAM roles, network, etc.

MLOps Architecture

Based on the above requirements from Equinix, key design considerations were made for example – using Vertex AI Feature Store instead of Big Query for online feature serving, using DataFlow for pre-processing vs using the existing python based pre-processing and so on. After carefully assessing all the alternatives, the below reference architecture for MLOps in GCP was proposed:

Reference architecture for MLOPs using GCP is illustrated in Figure 1. This architecture includes the following pipeline stages:

• Vertex AI Workbench enables data scientists to quickly explore new ideas and develop/experiment new models. The source code is saved in GitHub repository
• Github Actions with self-hosted runners are integrated with Github as the source code repository. This enables continuous integration of code, quality and security scans. The artifacts generated during the process are saved in Artifact Registry and Google Cloud Storage. These artifacts are deployed to implement the pipeline.
• Unit tests and integration tests can be performed during the continuous integration in Github Actions with self-hosted runners. End-to-end tests are performed on demand in the continuous integration pipeline. 
• Metadata about the artifacts is generated and saved in Vertex ML Metadata.
• Automated triggers can be enabled to run pipelines. For example, one trigger is the availability of new training data. These triggers can run the model training pipeline and the new trained model can be pushed to Vertex AI Model Registry.
  • To train a new ML model with new data, the deployed Vertex AI Pipeline is executed. 
  • To train a new ML model with new implementation, a new pipeline will be deployed through CI/CD pipeline.

“The proposed architecture design covers the requirements and scenarios that we were looking for. As our AI and ML portfolio is growing in scale and complexity, it’s important to follow a clear and up-to-date architecture if we want to keep increasing the value delivered by our solutions. As part of the process, the team also acquired the skills required to fully implement it” according to Bernardo Fernandes, Data Science Senior Manager at Equinix.

Below is the snapshot of the features before and after MLOps implementation at Equinix:

As businesses increasingly rely on machine learning to gain a competitive edge, MLOps has become a critical component of their strategy. By adopting MLOps practices, organizations can achieve faster time-to-market, better performance, and higher ROI for their machine learning initiatives.

Fast track end-to-end deployment with Google Cloud AI Services (AIS)

The partnership between Google Cloud and Equinix is just one of the latest examples of how we’re providing AI-powered solutions to solve complex problems to help organizations drive the desired outcomes. To learn more about Google Cloud’s AI services, visit our AI & ML Products page.

^{We’d like to give special thanks to Nitin Aggarwal, Vijay Surampudi, Parag Mhatre and Anantha Narayanan Krishnamurthy for their support and guidance throughout the project. We are also grateful to the super awesome collaboration with the Equinix Team (Ravi Pasula and Brendan Coffey, Bernardo Fernandes, Łukasz Murawski, Jakub Michałowski, Sonia Przygocka-Groszyk, Marek Opechowski, Daria Bondara, Nila Velu, Vijay Narayanan, Dharmendra Kumar, Shailesh Sukare, Arunraj Kumar Raje, Seng Cheong Lee).}